Dataset Descriptions - _ChoiceCZAL523

A) Reservation data :

i) Account : Account number for transactional purpose unique code (for cashiering and accounting personnel to reference).

ii) Guest Name : Name of a guest staying at hotel.

iii) Arrive : Guest arrival (entry) date.

iv) Depart : Guest exit date.

v) Nights : Total number of nights (days) a guest is staying.

vi) Status : Room current status

O (Occupied), R (Reserved), I (Inspected), N (No-Show), C (Cancelled) 1) No-Show: A reservation becomes a No-Show when the customer who has a guaranteed reservation does not cancel it before the hotel’s cancellation deadline, and never arrives to claim the reservation.

2) Occupied: Customer booked the room and actually he arrived as per check-in date.

3) Reserved: If the guest has not claimed or canceled the booked room by the specified time with hotel policies, you must hold the reserved rooms available until check-out time the following day.

4) Canclled: Customer has been cancelled the booked room successfully within the time period of cancellation policies.

5) Inspected: The main Purpose of a room inspection is to catch any problems that may have been overlooked during the cleaning before it is found by the guest and becomes a dissatisfaction and complaint. After guest check-out, manager is responsible for the room inspection before it allocated to new customer.

vii) Rate : The price of hotel charges for overnight accommodations (Room rates).

viii) Rate code : Room Rates and rate codes may also vary according to the available room features such as room size, location, view, furnishing, decors, competitors pricing. Similar to below references,

1) Early-bird Rate - This type of rates are only open X days before arrival. Eg: Open only when 7 Days before arrival.

2) Package Rate: Rates that includes a guest room in combination with other available events or activities. ( Eg: Best of London package which includes room rental, all meals, site seeing, airport transfers etc.)

3) Best Available Rates (BAR): These rate codes are the lowest discounted rate available for a day which can be offered to the guest by the Reservation or Front desk staff. BAR can be of different types

BAR Level         Open / Close when Occupancy Between              DATE          DAY      RATE
BAR - 01         0% TO 25 %                                       19/10/2016     WED     200.00
BAR - 02         26 % TO 35 %                                     20/10/2016     THU     200.00
BAR - 03         36% TO 50%                                       21/10/2016     FRI     250.00
BAR - 04         51% TO 75%                                       22/10/2016     SAT     250.00
BAR - 05         76% TO 100%                                      23/10/2016     SUN     250.00
                                                                  24/10/2016     MON     200.00
                                                                  25/10/2016     TUE     200.00

4) Family Rate: A rate reserved for families with children. Usually, these rates include Extra Bed charges and may also include some free add-on activities for children

ix) Room : Respetive room numbers assigned to rooms as per the hotel configurations.

x) Type :

1) NQQ - Double Queen Rooms (Non Smoking 2 Queen bed)

2) NK - Single King Rooms (Non Smoking Single King bed)

3) SNQQ - Double Queen Suites (Suites Non Smoking 2 Queen bed)

4) SNQQ1 - Double Queen Suites (Suites Non Smoking 2 Queen bed)

5) SNK - Single King Suites (Suites Non Smoking Single King bed)

6) NHQQ - Double Queen Room Handicap Equipped (Non Smoking Handicap 2 Queen bed)

7) NHK - Single King Room Handicap Equipped (Non Smoking Handicap Single King bed)

8) SNHK - Single King Suites Handicap Equipped (Suites Non Smoking Handicap Single King bed)

Note: i) 1 Queen bed --> Bed = 1 | Max adults = 2
     ii) 2 Queen bed --> Bed = 2 | Max adults = 4

ref: https://www.comfortinnofblueridge.com/accommodations/rooms/

xi) Source : The room booking mode which is used by guest

1) CRS Central Reservation System (online mode) :15714 2) DIRECT Direct (direct mode) :9330 3) WI Walk In (direct mode) :2423 xii) CRS Conf No : N.A

xiii) GTD : N.A

Guaranteed --> Prior to a function, the figure given by a meeting planner to the property for the number of persons to be served.

VI    7565
MC    4808
CC    4704
DB    3661
CA    2985
GM    2786
AX     673
DS     255
DP      13
CK       9
4P       6

xiv) Reserve Date : Room reservation date (On this day, guest reserve/ book the rooms by entering details as arrival, depart, adults, bed, etc)

xv) User : Receiption desk person id which is responsible for handling the guest activities

xvi) Shared Account : Is any shared / same account used for booking.

xvii) Track Code : Purpose for the room booking

Corporate (Meeting, signing, dealing) Leisure (Rest) Walk-in (Other purposes on quick decision) xviii) Package : Complete package cost for the guest including per person charge, inclusive of guest room, food and beverage, and proportional percentage of all other event charges.

(Packages seasonal - Winter, Spring, Summer) xix) Cancellation Date : The date on which cancelation request initiated for booked/ reserved room.

xx) CXL User ID : Person Id (receiption desk person/ individual) who is responsible to cancle the booking/ reservation room.

B) Occupancy data :

i) Date : Date of the occupancy

ii) Day : Day associated with date

iii) Rooms : Total number of the rooms in hotel

iv) OOO : OOO (Rooms that are in Out Of Order) is typically used when a room is being renovated, undergoing repairs and cannot be used.

v) StayOver : The guest is not expected to check out today and will remain at least one more night or may be week.

vi) Arrivals : Displays all guests arriving on a certain day or within a certain set time period.

vii) DueOut : The room is expected to become vacant after the following day’s checkout time.

viii) Available : Total numbers of available rooms (-ve sign indicates overdue)

ix) Group Block : Booking the rooms in a group or package (Ex. For event, people book multiple rooms)

x) Group Picked Up : Booking the rooms in a group.

xi) TransNGTD : NA.

*GTD: Guaranteed. Prior to a function, the figure given by a meeting planner to the property for the number of persons to be served.

xii) TransGTD : NA.

*GTD: Guaranteed. Prior to a function, the figure given by a meeting planner to the property for the number of persons to be served.

xiii) Occupied : Total number of rooms are in used/ occupied (Guest is currently registered to the room). [Rooms - Available - GroupBlock + GroupPickedUp]

xiv) OccPercentage : Room coocupany rate on perticular day (occupied rooms / total available rooms). The occupancy rate will tell you exactly how full your hotel is at any point in time.

xv) RoomRev : Total revenue generated from occupied rooms on perticulaar day

xvi) RevPAR : Revenue Per Available Rooms (total revenue / total available rooms). This metric provides a glimpse into the number of rooms that are being sold at a hotel and how much revenue is being generated from those bookings. You should use RevPAR to understand the best way to maximise the revenue generated per room. If the RevPAR of your property is increasing, it must mean your average room rate or occupancy rate is increasing – or both!

xvii) ADR : Average daily rate of guest on perticular day (total revenue / rooms occupied). it is calculated using the amount of revenue earned and the number of rooms sold to give you an average rate. [Ref: https://www.siteminder.com/r/calculate-revpar/]

1) ADR will simply tell you how much revenue each sold room is selling for on average, while RevPAR will tell you how much revenue you’re bringing in for all your rooms.

2) Since ADR simply indicates the price of your rooms while RevPAR will tell you how much money you yield from each room, sold or not.

viii) Ppl : NA.

**Formulaes Details - Traditionally, the effectiveness of revenue management strategy is measured using the following KPIs

a) Occupancy rate — the number of occupied rental units at a given time, compared to the total number of available rental units at that time.

Occupancy rate = Rooms sold / Room available

b) Average daily rate (ADR) — this rate is applied to a room’s average rental income during a certain period. It’s compared to hotel’s historical ADR or competitors’ metrics.

ADR = Rooms revenue earned / Number of rooms sold

c) Revenue per available room (RevPAR) — a KPI that assess financial and business performance of a hotel. RevPAR measures ability of a property to fill all the rooms and define the best price for them. There are two ways to calculate it:

RevPAR = Rooms revenue / Rooms available
RevPAR = Average daily rate * Occupancy rate

d) Total revenue per available room (TRevPAR) — a metrics that accesses total revenue, generated by property and based on room cost and money spent on it. This KPI captures a snapshot of overall business performance. TRevPAR is one of the main benchmarking tools for big hotels and resorts.

The higher the TRevPAR, — the better the revenue.
TRevPAR = Total revenue / Total number of available rooms

e) Net revenue per available room (NRevPAR) — a KPI that allows hotel revenue managers to calculate the distribution cost to see how the room revenue is generated. NRevPAR includes spending on marketing and distribution.

NRevPAR = (Room revenue — distribution costs) / Number of available rooms

f) Gross operating profit per available room (GOPPAR) — measures the profit of a hotel and value of all assets at any given time. GOPPAR measures profit to capacity, including all a hotel’s spending and taxes.

GOPPAR = Gross Operating Profit / Number of available rooms

Ref: https://www.siteminder.com/r/calculate-revpar/ | https://www.upstay.tech/hotel-revenue-management-formulas-kpis-calculations-use-cases/

A) Library And Data Loading

A1 - Library importing

Importing the required libraries and supportive packages

'''
Library and packages

'''

A2 - Data loading from the source

Importing the data '.txt' from the source and save in '.csv' format

'''
Reservation data and Occupancy data

'''

B) Data Preparation

As we have the raw data and need to analyze, prepare for business case studies. This stage involves data preprocessing, data cleaning and data understanding.

• Raw data is not in acceptable format, we need to verify the data quality and convert into acceptable format as per Machine Learning algorithms.

B1 - Reservation data

• We are going to clean data and prepare data in required formats. So that we can analyze the data.

Data pre-processing steps:

i) Column rename

ii) Conversion of dataypes

iii) Replacenemt of unnecessary data values and EDA

iv) Missing value analysis

v) Date param extractions

vi) Data transformations and experiments

vii) Data insights

i) Reservation data size

'''
Size of reservation data

'''

Dimenssion of Reservation data : (40604, 7)

Observation:

• Data has around 40K records and 9 columns.

ii) Unique values in room types, rate codes, track codes

'''
Number of unique data categories?

'''

RateCode     103
Type           8
Source         3
TrackCode      4
dtype: int64

Observations:

In given property data,

• We have total 8 different room types.
• We have total 100+ different available rates codes.
• We have total 4 different types of track codes.
• We have total 3 different sources.

iii) Analysis of room types

'''
Type of rooms

'''

Observations:

• Room type NQQ has the most contribution from available types of rooms in reservation data.
• Room type SNHK has the least contribution from available types of rooms in reservation data.

iv) Analysis of rate codes

'''
Type of rate codes

'''

Observations:

• Rate code BAR has the most contribution from series of different types of rate codes.

iv) Analysis of track codes

'''
Type of track code

'''

Observations:

• Track code Leisure has the most contribution in given data.
• Track code Meeting rooms has the least contribution in given data.

v) Analysis of nights (stay)

'''
Total nights or stays analysis from data

'''

Night (stays)	1	2	5	3	4	6	7	8
Total counts	25790.00	6923.00	2551.00	2339.00	2036.00	310.00	187.00	82.0
% counts	63.52	17.05	6.28	5.76	5.01	0.76	0.46	0.2

Observations:

• From data, maximum times people prefer one night stay the most in hotel.

B2 - Occupancy data

• We are going to clean data and prepare data in required formats. So that we can analyze the data.

Data pre-processing steps:

i) Column rename

ii) Conversion of dataypes

iii) Replacenemt of unnecessary data values

iv) Missing value analysis

v) Date param extractions

vi) Data transformations and experiments

vii) Data insights and EDA

'''
Occupancy data size

'''

Dimenssion of Occupancy data : (2922, 18)

Observation:

• Data has around more than 2K records and 18 columns.

B3 - Combination dataset

• After analyzing data, Occupancy % is one of the important factor here. So we have date-wise occupancy, room rates, room types, rate codes and other piece of informations.

Observation:

• Now we have cleaned data with features Date, Year, Month, Days, Nights, Type, RateCode, TrackCode, Rate, Occupied, ADR.

We have 4 dataypes, and for machine learning, we need all inputs in numeric format strickly

Column Name     Datatype format
-------------------------------
Date            datetime
Year            int
Month           int
Days            int
Nights          int
Type            object
TrackCode       object
RateCode        object
Rate            float
Occupied        int
ADR             float

• From above 3 columns are non-numeric Type, RateCode, TrackCode. For this columns, we need to convert its data into number format [Machine Learning Methods - Label encoding, One-hot encoding, Label binarizer, etc...]

B4 - Yield Dataset

• In this step, we are loading yield data and based on this yield data, we extract the yeild % as per check-in slot and occupancy slot.

i) Load yield data

'''
Below table is the yield data representation

'''

	OccupancyLevel	0Day	1Day	2Day	7Day	15Day	30Day	60Day	90Day	MaxDay
0	0-10	-0.20	-0.17	-0.15	-0.12	-0.10	-0.07	-0.05	-0.02	0.02
1	11-20	-0.17	-0.15	-0.12	-0.10	-0.07	-0.05	-0.02	0.02	0.05
2	21-30	-0.15	-0.12	-0.10	-0.07	-0.05	-0.02	0.02	0.05	0.07
3	31-40	-0.12	-0.10	-0.07	-0.05	-0.02	0.02	0.05	0.07	0.10
4	41-50	-0.10	-0.07	-0.05	-0.02	0.02	0.05	0.07	0.10	0.12
5	51-60	-0.07	-0.05	-0.02	0.02	0.05	0.07	0.10	0.12	0.15
6	61-70	-0.05	-0.02	0.02	0.05	0.07	0.10	0.12	0.15	0.17
7	71-80	-0.02	0.02	0.05	0.07	0.10	0.12	0.15	0.17	0.20
8	81-90	0.02	0.05	0.07	0.10	0.12	0.15	0.17	0.20	0.20
9	91-100	0.05	0.07	0.10	0.12	0.15	0.17	0.20	0.20	0.20

ii) Get yeild % values (based on check-in slot and occupancy level)

B5 - Added holiday - (Region = US)

Here, we are adding up the holidays in US region from python the package.

Package name           : holidays
Desciption             : US federal holiday calendar

ref: https://pypi.org/project/holidays/

Note: Initially we checking out (1) how much model learn/ capture this holiday effect? (2) And there is price change with this effect or not? (3) Holiday data is very very low as compared to actual data length, but with this initial data, we are trying to see is chance is happend or not?

$\color{Blue}{\text{,,}}$

i) US federal holiday calendar

'''
Holiday dates and descriptions

'''

2022-01-01 - New Year's Day
2022-01-17 - Martin Luther King Jr. Day
2022-02-21 - Washington's Birthday
2022-05-30 - Memorial Day
2022-06-19 - Juneteenth National Independence Day
2022-06-20 - Juneteenth National Independence Day (Observed)
2022-07-04 - Independence Day
2022-09-05 - Labor Day
2022-10-10 - Columbus Day
2022-11-11 - Veterans Day
2022-11-24 - Thanksgiving
2022-12-25 - Christmas Day
2022-12-26 - Christmas Day (Observed)

Obsercation:

• Above are the holidays in year 2022 as per US federal holiday calendar and py package.

B6 - Date Features Extractions

Here, we are extracting Day of week, weekend, month name, day name, week name, etc... from Date column.

i) Extraction of data parameters

ii) Pre-processing data

iii) Validate data quality

iV) Analyzing the data and EDA

Note: Direct Date (datatype: datetimens) is not handle by ML model. Machine Learning algortihm expects data is in numeric form. Hence we are extracting requied fields from Date directly.

B7 - Event addition - (Region - US | Type - Sports)

• We are adding sporting events from external excel file.

Currently taking event data from web portal and save event data from date range 2017 to 2027 in excel file.

ref: https://www.timeanddate.com/holidays/us/super-bowl

Ex. (1) With reference link, in year 2022 below are the major sporting events.

13 Feb  Sunday    Super Bowl               Sporting event    
18 Apr  Monday    Boston Marathon          Sporting event    
6 May   Friday    Kentucky Oaks            Sporting event    
7 May   Saturday  Kentucky Derby           Sporting event    
21 May  Saturday  Preakness Stakes         Sporting event    
11 Jun  Saturday  Belmont Stakes           Sporting event    
6 Nov   Sunday    New York City Marathon   Sporting event

Note: Initially we checking out (1) how much model learn/ capture this effect? (2) And there is price change with this effect or not? (3) Event data is very very low as compared to actual data length, but with this initial data, we are trying to see is chance is happend or not?

C) Features Encoder

As Machien Learning algorithms expects all inputs must be in numeric format, hence in this step, we are converting non-numeric (Obejct) form to numeric (int) using AI techniques.

D) Modeling Part

In this section, we are applying Machine Learning algorithms to our data. The business case is related to prediction, hence applying ML Regression Algorithms and see the results on given data.

Input features : Year, Month, Days, Nights, Type, RateCode, TrackCode, Occupied

Output feature : Rate

Models : Linear model, Decision model, Boosting model

ML algorithms : LinearRegression, SVR, RandomForestRegression, GradientBoostingRegressor, XGBRegressor, KNeighborsRegressor

Matrics : MAE, RMSE, R2

Strategy - Considering 100% scale, about 70% data is use for model training purpose and remaining 30% data is use for testing purpose. So model is build on 70% of data and we will test the model (how model is being perform/ model behaviour) on 30% data which is unseen by model. This section is base model

ref: https://scikit-learn.org/stable/index.html

'''
Data spliting for Machine Learning models

'''

D3 - ML models and matric results

Models :

• Linear model - Linear regression
• Tree based model - Decision tree regressor
• Ensemble model (Bagging) - Random forest regressor
• Ensemble model (Boosting) - Xgb regressor, gradient boosting regressor
• Distance measures model - Knn regressor

Cross Validation :

• CV - KFold with value 5

Accuracy measures :

• R2 score

Error measure :

• MAE and RMSE

Keypoints - For best model performance, below are the points to be consider for model selection as

• Accuracy score should be high
• CV mean score should be high and quite similar to testing score
• Error measures should be low
• Model should not be in overfit and underfit
• Generalize model should be prefer

ref: https://scikit-learn.org/stable/model_selection.html#model-selection

'''
Machine Learning Model 1

'''

ML Algorithm     : LinearRegression()
R2 Score (train) : 0.35311734277965434
R2 Score (test)  : 0.3454420452832617
CV Scores        : [0.32823183 0.37244873 0.34333893 0.35166847 0.34916579]
CV Scores mean   : 0.3489707491837339
MAE rate         : 16.721313679138696
MSE rate         : 483.75551118371715
RMSE rate        : 21.994442734102567

Wall time: 334 ms

	Actual	Prediction	Diff
5834	104.50	103.774546	0.73
35404	110.00	96.340908	13.66
12992	144.32	110.330746	33.99
36147	99.00	89.018656	9.98
30203	125.10	106.115283	18.98
...	...	...	...
28410	90.00	86.215416	3.78
14431	70.00	76.870664	-6.87
17721	99.00	98.217732	0.78
1324	65.00	73.770245	-8.77
1559	94.05	95.715983	-1.67

11925 rows × 3 columns

'''
Machine Learning Model 2

'''

ML Algorithm     : DecisionTreeRegressor()
R2 Score (train) : 0.9696395461357734
R2 Score (test)  : 0.774123806817491
CV Scores        : [0.78076799 0.78132714 0.77070523 0.77360479 0.76227651]
CV Scores mean   : 0.7737363320700531
MAE rate         : 6.421869970986376
MSE rate         : 166.93533782585078
RMSE rate        : 12.920345886463364

Wall time: 2.61 s

	Actual	Prediction	Diff
5834	104.50	97.950000	6.55
35404	110.00	110.000000	0.00
12992	144.32	125.000000	19.32
36147	99.00	89.000000	10.00
30203	125.10	122.550000	2.55
...	...	...	...
28410	90.00	90.000000	0.00
14431	70.00	109.857143	-39.86
17721	99.00	99.000000	0.00
1324	65.00	65.000000	0.00
1559	94.05	125.550000	-31.50

11925 rows × 3 columns

'''
Machine Learning Model 3

'''

ML Algorithm     : RandomForestRegressor()
R2 Score (train) : 0.955801467567185
R2 Score (test)  : 0.84451992913283
CV Scores        : [0.84680105 0.85130809 0.84663149 0.8446779  0.84471015]
CV Scores mean   : 0.846825734258647
MAE rate         : 5.706260409024578
MSE rate         : 114.90860453109552
RMSE rate        : 10.719543112049857

Wall time: 1min 20s

	Actual	Prediction	Diff
5834	104.50	95.535950	8.96
35404	110.00	110.931429	-0.93
12992	144.32	131.873800	12.45
36147	99.00	88.898238	10.10
30203	125.10	124.260000	0.84
...	...	...	...
28410	90.00	90.000000	0.00
14431	70.00	76.117600	-6.12
17721	99.00	99.000000	0.00
1324	65.00	65.000000	0.00
1559	94.05	101.717297	-7.67

11925 rows × 3 columns

'''
Machine Learning Model 4

'''

R2 Score (train) : 0.8938754536203146
R2 Score (test)  : 0.8560536031167904
CV Scores        : [0.85679053 0.85825166 0.85907572 0.85857106 0.86630919]
CV Scores mean   : 0.8597996296962591
MAE rate         : 6.579201321672508
MSE rate         : 100.45588154579252
RMSE rate        : 10.022768157839057

Wall time: 24.9 s

	Actual	Prediction	Diff
68111	101.412908	105.944046	-4.53
4819	112.075894	104.399254	7.68
57305	89.100000	100.965729	-11.87
36592	76.037644	77.574059	-1.54
70129	100.000000	125.763565	-25.76
...	...	...	...
430	99.000000	132.476593	-33.48
18238	110.000000	120.430710	-10.43
10251	118.703046	113.550629	5.15
56733	114.000000	108.585419	5.41
53303	80.797862	85.859436	-5.06

18153 rows × 3 columns

'''
Machine Learning Model 5

'''

ML Algorithm     : KNeighborsRegressor()
R2 Score (train) : 0.8390347527774555
R2 Score (test)  : 0.7474403731818305
CV Scores        : [0.76321282 0.76464483 0.7512027  0.76124977 0.75407274]
CV Scores mean   : 0.7588765720187757
MAE rate         : 7.888033542976939
MSE rate         : 186.6559110547589
RMSE rate        : 13.662207400517637

Wall time: 7.27 s

	Actual	Prediction	Diff
5834	104.50	96.142	8.36
35404	110.00	109.000	1.00
12992	144.32	90.308	54.01
36147	99.00	89.800	9.20
30203	125.10	119.700	5.40
...	...	...	...
28410	90.00	90.000	0.00
14431	70.00	70.000	0.00
17721	99.00	99.000	0.00
1324	65.00	65.000	0.00
1559	94.05	104.236	-10.19

11925 rows × 3 columns

'''
Machine Learning Model 6

'''

ML Algorithm     : GradientBoostingRegressor()
R2 Score (train) : 0.7627236935074548
R2 Score (test)  : 0.7590169985129133
CV Scores        : [0.7646562  0.75746387 0.75574216 0.76197451 0.75006547]
CV Scores mean   : 0.7579804411893672
MAE rate         : 9.648806972266968
MSE rate         : 178.10012731633682
RMSE rate        : 13.345415966403475

Wall time: 29.3 s

	Actual	Prediction	Diff
5834	104.50	102.145549	2.35
35404	110.00	112.841109	-2.84
12992	144.32	116.144812	28.18
36147	99.00	97.072799	1.93
30203	125.10	120.867691	4.23
...	...	...	...
28410	90.00	94.731002	-4.73
14431	70.00	98.006643	-28.01
17721	99.00	96.071330	2.93
1324	65.00	64.372914	0.63
1559	94.05	99.527762	-5.48

11925 rows × 3 columns

D4 - Model performanace comprision

We are comparing the model performaces, so that best model we can choose for tunning and predictions.

• We are comparing models on Testing score, CV score, MAE rate and RMSE rate. Testing score (high), CV mean score (high), MAE and RSME rates (low).
• Also we need to verify is model is overfit or underfit, as we are selcting generalize model. For this, we are comparing train score and test score And prior to that Cross validation score.

Table columns:

• Training score refers how model is perform well on training data during model learning process. As training accuracy increases, model learn more and more. But need more data in training, so that it can capture form various datapoints and fit the values in ML equations based on selection of ML algorithm.

• Testing score refers how model is perform well on testing data after the training phase. As model learned from training data during training phase, we need to validate how model perform on unseen or new data. So that we can check this by testing accuracy. High testing accuracy should be prefer because as the value is high, our model is well fit on new data also.

• CV score refers cross validation score. In model training and testing, it might possible that few of datapoints are present in training but not in testing and in this senario, model is not able to predict accurately. Hence to resolve this conflict, are are using cross validation techniques by library "sklearn". It uses multiple folds of training and testing points.

ref: (Cross validation) https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation

• MAE, MSE, RMSE refers the error measure matric for regression business case. MAE(Mean Absolute Error), MSE(Mean Squared Error) and RMSE(Root Mean Squared Error) shlould be low as possible as. Low value meaning error rate is minimum in the process of model predictions. We generally prefer RMSE and MAE.

'''
Machine Learning Model performanance analysis

'''

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
             importance_type='gain', interaction_constraints='',
             learning_rate=0.300000012, max_delta_step=0, max_depth=6,
             min_child_weight=1, missing=nan, monotone_constraints='()',
             n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
             tree_method='exact', validate_parameters=1, verbosity=None)

	ML Algorithms	Training Score	CV Mean Score	Testing score	MAE Rate	MSE Rate	RMSE Rate
0	XGBRegressor(base_score=0.5, booster='gbtree',...	0.889591	0.845926	0.846648	6.769352	113.335624	10.645921
1	(DecisionTreeRegressor(max_features='auto', ra...	0.955801	0.846826	0.844520	5.706260	114.908605	10.719543
2	DecisionTreeRegressor()	0.969640	0.773736	0.774124	6.421870	166.935338	12.920346
3	([DecisionTreeRegressor(criterion='friedman_ms...	0.762724	0.757980	0.759017	9.648807	178.100127	13.345416
4	KNeighborsRegressor()	0.839035	0.758877	0.747440	7.888034	186.655911	13.662207
5	LinearRegression()	0.353117	0.348971	0.345442	16.721314	483.755511	21.994443

Observations:

• Ensemble model perform well as compared to linear model and simple tree based model.
• Top 2 models are RFR and XGB
• For RandomForestRegressor, Accuracy is 83% with RMSE score (error measures - root) is 10 and MAE (error measure - absolute) is 5.
• For XgbRegressor, Accuracy is 84% with RMSE score (error measures - root) is 10 and MAE (error measure - absolute) is 6.
• Also CV score is good for both model about on average 83.

Note: As top 2 models are XGBRegressor (83%) + RandomForestRgressor (83%). Previously we got 80% accuracy, here we improve accuracy about 3%. For finding best situable parameter, we need to perform hyperparameter tunning and using this we can enhance our model with best results.

'''
Selection of model

'''

E) Instance Balances Nature

Experiment on instance / data balancing and then approaching towards the modeling

• In this section, there is data balancing experiment for the Room types that are available in our dataset. Wr are using imblearn library and its different selective methods.

ref: https://imbalanced-learn.org/stable/references/over_sampling.html

E1 - Method I - Data points balancing

'''
Class distrubution in data (before applying ML technique)

'''

Class=5, n=2787 (7.012%)
Class=3, n=16160 (40.657%)
Class=0, n=842 (2.118%)
Class=2, n=11455 (28.820%)
Class=7, n=2975 (7.485%)
Class=6, n=3770 (9.485%)
Class=4, n=404 (1.016%)
Class=1, n=1354 (3.407%)

Observation:

• From the figure, around 40% of data points are belongs to room type class 3 while minimal data points belongs to room type class 4. This indicates unbalance natures of room data points.
• We are conducting instace balance experiment and trying out the modeling once we have balanced data points.

'''
Class distrubution in data (after applying ML technique)

'''

Class=5, n=16160 (12.500%)
Class=3, n=16160 (12.500%)
Class=0, n=16160 (12.500%)
Class=2, n=16160 (12.500%)
Class=7, n=16160 (12.500%)
Class=6, n=16160 (12.500%)
Class=4, n=16160 (12.500%)
Class=1, n=16160 (12.500%)

Observation:

• From the figure, all the room types (all the classes) are contributing in the same counts (12% each) i.e., this indicates balanced data points in nature.

E2 - Method I - Modeling part

'''
Machine Learning Model 1 

'''

Training score :  0.8745748050840594
Testing score  :  0.8535179553573975
CV score       :  0.8567685849825789
MAE score      :  6.632022970710143
RMSE score     :  10.255269604586836
Wall time: 48.1 s

'''
Machine Learning Model 2 

'''

Training score :  0.9793532202028591
Testing score  :  0.9132275765652069
CV score       :  0.9187644925110348
MAE score      :  3.5940022690171674
RMSE score     :  7.893066266251923
Wall time: 6min 16s

'''
Machine Learning Model 3

'''

Training score :  0.9205924751638159
Testing score  :  0.8695406802808101
CV score       :  0.8787533460366079
MAE score      :  4.522488247984103
RMSE score     :  9.678152036944146
Wall time: 28.4 s

'''
Machine Learning Model 4

'''

Training score :  0.7541828431166233
Testing score  :  0.7485207184275926
CV score       :  0.7507311202505519
MAE score      :  9.657718644779559
RMSE score     :  13.4371136682871
Wall time: 1min 19s

E3 - Method II - Data points balancing

'''
Class distrubution in data (before applying ML technique)

'''

Class=5, n=2787 (7.012%)
Class=3, n=16160 (40.657%)
Class=0, n=842 (2.118%)
Class=2, n=11455 (28.820%)
Class=7, n=2975 (7.485%)
Class=6, n=3770 (9.485%)
Class=4, n=404 (1.016%)
Class=1, n=1354 (3.407%)

Observation:

• From the figure, around 40% of data points are belongs to room type class 3 while minimal data points belongs to room type class 4. This indicates unbalance natures of room data points.
• We are conducting instace balance experiment and trying out the modeling once we have balanced data points.

'''
Class distrubution in data (after applying ML technique)

'''

Class=5, n=16105 (12.437%)
Class=3, n=16160 (12.480%)
Class=0, n=16150 (12.472%)
Class=2, n=14845 (11.464%)
Class=7, n=16587 (12.809%)
Class=6, n=17320 (13.375%)
Class=4, n=16249 (12.548%)
Class=1, n=16076 (12.415%)

Observation:

• From the figure, all the room types (all the classes) are contributing in the around similar values approx adaptively

E3 - Method II - Modeling part

'''
Machine Learning Model 1

'''

Training score :  0.8731162214879863
Testing score  :  0.8489802923910031
CV score       :  0.8500394846448881
MAE score      :  6.73801066185894
RMSE score     :  10.33483522424665
Wall time: 47.5 s

'''
Machine Learning Model 2

'''

Training score :  0.9792815507464897
Testing score  :  0.9103913993659695
CV score       :  0.9148825285849524
MAE score      :  3.7761393342396534
RMSE score     :  7.96088942721416
Wall time: 5min 14s

'''
Machine Learning Model 3

'''

Training score :  0.9155600141742807
Testing score  :  0.8644597850073013
CV score       :  0.8718770580740648
MAE score      :  4.7226115720474935
RMSE score     :  9.790859744763223
Wall time: 27.9 s

'''
Machine Learning Model 4

'''

Training score :  0.7452501601913991
Testing score  :  0.7414846585541073
CV score       :  0.7416884782824307
MAE score      :  9.684744928204143
RMSE score     :  13.521656678162675
Wall time: 1min 23s

E4 - Method III - Data points balancing

'''
Class distrubution in data (before applying ML technique)

'''

Class=5, n=2787 (7.012%)
Class=3, n=16160 (40.657%)
Class=0, n=842 (2.118%)
Class=2, n=11455 (28.820%)
Class=7, n=2975 (7.485%)
Class=6, n=3770 (9.485%)
Class=4, n=404 (1.016%)
Class=1, n=1354 (3.407%)

Observation:

• From the figure, around 40% of data points are belongs to room type class 3 while minimal data points belongs to room type class 4. This indicates unbalance natures of room data points.
• We are conducting instace balance experiment and trying out the modeling once we have balanced data points.

'''
Class distrubution in data (after applying ML technique)

'''

Class=0, n=12530 (17.819%)
Class=1, n=11097 (15.781%)
Class=2, n=5438 (7.734%)
Class=3, n=4883 (6.944%)
Class=4, n=13755 (19.561%)
Class=5, n=8074 (11.482%)
Class=6, n=6741 (9.587%)
Class=7, n=7799 (11.091%)

Observation:

• From the figure, we can se that the majority data points are minimized by few % while thoose of minority data points are maximized by few %.

E4 - Method III - Modeling part

'''
Machine Learning Model 1

'''

Training score :  0.8941397629744505
Testing score  :  0.8660863263330365
CV score       :  0.8697151157669074
MAE score      :  6.356461429731838
RMSE score     :  9.664947005443487
Wall time: 28.4 s

'''
Machine Learning Model 2

'''

Training score :  0.9897971557861857
Testing score  :  0.9419249084930137
CV score       :  0.9466292200032888
MAE score      :  2.7520375622818656
RMSE score     :  6.364759036266756
Wall time: 2min 31s

'''
Machine Learning Model 3

'''

Training score :  0.7387654528118385
Testing score  :  0.734542822239673
CV score       :  0.73430856544963
MAE score      :  9.923953411873862
RMSE score     :  13.60768512455801
Wall time: 1min 18s

E5 - Model performanace comprision (data balanced)

'''
Machine Learning Model performanance analysis

'''

RFR

	model	resample	trainingScore	testingScore	cvScore	maeRate	rmseRate
0	RFR	SMOTEENN	0.989797	0.941925	0.946629	2.752038	6.364759
1	RFR	SMOTE	0.979353	0.913228	0.918764	3.594002	7.893066
2	RFR	ADASYN	0.979282	0.910391	0.914883	3.776139	7.960889
3	KNN	SMOTEENN	0.942827	0.904470	0.912219	3.685423	8.163129
4	KNN	SMOTE	0.920592	0.869541	0.878753	4.522488	9.678152
5	XGB	SMOTEENN	0.894140	0.866086	0.869715	6.356461	9.664947
6	KNN	ADASYN	0.915560	0.864460	0.871877	4.722612	9.790860
7	XGB	SMOTE	0.874575	0.853518	0.856769	6.632023	10.255270
8	XGB	ADASYN	0.873116	0.848980	0.850039	6.738011	10.334835
9	GBR	SMOTE	0.754183	0.748521	0.750731	9.657719	13.437114
10	GBR	ADASYN	0.745250	0.741485	0.741688	9.684745	13.521657
11	GBR	SMOTEENN	0.738765	0.734543	0.734309	9.923953	13.607685

Observation:

We are comparing the model performaces, so that best model we can choose for tunning and predictions.

After data balacing mechanisms, we have seen the improvemtents in model performances as compared to the without effect of the balacing. But in this techniques there is chances of data points duplications and removals, as for balacing the data using AI techniques, different types of algorithms uses its internal procedure (Ex. targeting large values, distance based similarities, etc.).

• With balancing nature of data, we have seen around 3% to 7% improvements in the model accuracy.
• Error rate is also minimized around by 3 to 5 units.
• We want the optimistic model (generalized model which can deal with new data efficiently with good results) as by this techniques, accuracy is improved for similar kind of data, but in future if data points may changes, it leads model performances and arrises issue of overfitting and underfitting.

E12 - Model performanace comprision (data balanced + duplicaions removal)

'''
Machine Learning Model performanance analysis

'''

RFR

	model	resample	trainingScore	testingScore	cvScore	maeRate	rmseRate
0	RFR	SMOTEENN	0.988785	0.930989	0.933843	3.192555	6.908432
1	RFR	SMOTE	0.979931	0.898479	0.900772	4.194470	8.474127
2	RFR	ADASYN	0.979573	0.894553	0.899120	4.260626	8.569204
3	KNN	SMOTEENN	0.934814	0.894007	0.898520	3.954146	8.561704
4	XGB	SMOTEENN	0.894779	0.858841	0.864455	6.508719	9.880412
5	KNN	ADASYN	0.904166	0.847872	0.854149	5.191092	10.292665
6	KNN	SMOTE	0.907778	0.846231	0.856235	5.153720	10.429196
7	XGB	SMOTE	0.871480	0.843137	0.844500	6.861812	10.533588
8	XGB	ADASYN	0.865587	0.836351	0.838462	7.015679	10.675298
9	GBR	SMOTE	0.746092	0.743788	0.744723	9.577961	13.462192
10	GBR	SMOTEENN	0.740549	0.737515	0.735846	9.691569	13.473273
11	GBR	ADASYN	0.739438	0.733639	0.738456	9.730570	13.619417

Observation:

We are comparing the model performaces, so that best model we can choose for tunning and predictions.

After data balacing mechanisms and duplicate datapoints/ records removal, we have seen the accuracy drop by 1 to 3 units and error increment by 1 to 3 units as compared to the pevious data balacing experiment.

• This is happend due to minimized the number of records and cleaned data as no duplication in data. But even if duplication is not there, there are chances of records that are deleted from data by data balancing AI algorithms as its internal working nature.

Results without eliminating duplicates data points as,

        model    resample    trainingScore   testingScore   cvScore     maeRate     rmseRate
    9   RFR      SMOTEENN    0.989901        0.938760       0.946618    2.886929    6.538762
    1   RFR      SMOTE       0.979595        0.914869       0.918256    3.582198    7.786674
    5   RFR      ADASYN      0.979324        0.911031       0.913912    3.740118    7.994166
    10  KNN      SMOTEENN    0.944063        0.898050       0.912317    3.794717    8.436666
    2   KNN      SMOTE       0.919330        0.869398       0.878157    4.525902    9.644561
    6   KNN      ADASYN      0.916140        0.866234       0.872831    4.687299    9.802282
    8   XGB      SMOTEENN    0.897690        0.864710       0.869792    6.503276    9.718727
    0   XGB      SMOTE       0.877415        0.853478       0.857020    6.585655    10.215493
    4   XGB      ADASYN      0.869149        0.848388       0.848799    6.767652    10.435700
    3   GBR      SMOTE       0.751074        0.750621       0.747344    9.574245    13.327178
    7   GBR      ADASYN      0.745340        0.744134       0.744509    9.762927    13.556919
    11  GBR      SMOTEENN    0.743594        0.736216       0.739159    10.040380   13.570646

'''
Model selected for demonstration usages only

'''

model                 RFR
resample         SMOTEENN
trainingScore    0.988785
testingScore     0.930989
cvScore          0.933843
maeRate          3.192555
rmseRate         6.908432
Name: 0, dtype: object

Observation:

• As RFR model and SMOTEENN technique on data-side gives best performance model results. So we are currently selecting this for further analysis continuation.

Note: Hyperparam and model testing is pending...

F) Effect of holiday + event on prediction..

Let's analyze how is the model perform in combination of event and holiday in past data (testing dataset). So that we can know the effect of those combinations is work or not.

• Currently holiday and event data is low, hence it might be seen the effect or not for this time. Once we increasing the data, it might change the results.

Note: Every time we need to pass new data index and new value manually which is present in testing data. (because we previously used shuffle method.)

F1 - Analysis using holiday / event effects on prices in model on model test dataset

i) Data seperation : holiday = 1 / event = 0

• Selecting the records from the test data where holiday is present (1) and event is not present (0)
• Trying to predict with this combination (might be impacted or not, depending upon the how model is learned from these feature variacnes)

Holiday consideration (previous obs)

• If there is holiday but not event, price = 71.82
• If holiday with event, price = 71.77 ...... (not impacted)

• If alone there is event, price = 73.95 .....(impacted with event only +2)

  [71.8224] ........dataframe -> holiday (1) + event (0) [71.8224] ........holiday (1) + event (0) [73.9591] ........holiday (0) + event (1) [73.9961] ........holiday (0) + event (0) [71.7774] ........holiday (1) + event (1)

ii) Data seperation : holiday = 0 / event = 1

• Selecting the records from the test data where holiday is not present (0) and event is present (1).
• Trying to predict with this combination (might be impacted or not, depending upon the how model is learned from these feature variacnes)

Event consideration (previous obs)

• If there is event but not holiday, price = 70.00
• If event with holiday, price = 72.94 ...... (impacted with event and holiday +2)

• If alone there is holiday, price = 71.61 .....(not much impacted with holiday only +1)

  [71.8224] ........dataframe -> holiday (1) + event (0) [71.8224] ........holiday (1) + event (0) [73.9591] ........holiday (0) + event (1) [73.9961] ........holiday (0) + event (0) [71.7774] ........holiday (1) + event (1)

iii) Data seperation : holiday = 0 / event = 0

• Selecting the records from the test data where both holiday and event is not present (0).
• Trying to predict with this combination (might be impacted or not, depending upon the how model is learned from these feature variacnes)

Business days consideration (previous obs)

• If there is no event and no holiday (business days), price = 83.00 If alone there is alone holiday, price = 92.72 .....(impacted with holiday only +9)
• If alone there is event only, price = 83.28 .....(not impacted +0.28)

• If there is event and holiday, price = 92.58 ...... (impacted with event and holiday +9)

  [71.8224] ........dataframe -> holiday (1) + event (0) [71.8224] ........holiday (1) + event (0) [73.9591] ........holiday (0) + event (1) [73.9961] ........holiday (0) + event (0) [71.7774] ........holiday (1) + event (1)

iv) Data seperation : holiday = 1 / event = 1

• Selecting the records from the test data where both holiday and event is present (1).
• Trying to predict with this combination (might be impacted or not, depending upon the how model is learned from these feature variacnes)

Holiday and event consideration (previous obs)

• If event with holiday, price = 72.54
• If there is event but not holiday, price = 73.34 .....(impacted with event only +1) If alone there is holiday, price = 73.06 .....(impacted with holiday only +1)

• If no holiday and no event, price = 73.13 .....(impacted with both +1, but effect is smaller than alone event)

  [71.8224] ........dataframe -> holiday (1) + event (0) [71.8224] ........holiday (1) + event (0) [73.9591] ........holiday (0) + event (1) [73.9961] ........holiday (0) + event (0) [71.7774] ........holiday (1) + event (1)

Observation: On multiple time simulation in historical test data, below are the observation,

• There is change in predicted price over the event and holiday case, only event case, only holiday case.
• The change is happend minimum, might be possible as holiday and event data samples are very minimum as compared to total data size.
• But model is learned this small external effects and thats why we are able to see change in predicted price with event and holiday simulations.

G) Hyperparam

• This section proveides the information about which are the best parameters for given data and algorithm, so that we can improve the results from previous abse model. In base model, model parameter are consist of default values and data is fitted using default param. But in hyperparameter tunning, the searching algorithm finds the best situalble paramts rather than default one. So that using these best param, we can improve the results of model.

• Hyperparameter tuning is an essential part of controlling the behavior of a machine learning model. If we don’t correctly tune our hyperparameters, our estimated model parameters produce suboptimal results, as they don’t minimize the loss function. This means our model makes more errors.

Methods of tunning:

1) Grid search

2) Random Search

3) Bayesian optimizations

ref: https://www.anyscale.com/blog/what-is-hyperparameter-tuning

Note: Commenting this while section as it takes all available machine CPU cores (all physical cores), avaliable memory (above 98% RAM) and lots of time (multiple hours depending upon the parameter define, its all combinations and total passed data size)

'''
Take lot of time to as per resource available

'''

H) Model Results

With param tuuning / without param tunning

'''
Not applicable as depend on Section F)

'''

I) User inputs

I1 - Data extraction based on user inputs

This section provides the simulation based on user inputs. User need to submit inputs and based on inputs, model is trying to predict the Room Rate.

I2 - Give user inputs here...

• Arrival date : yyyy-mm-dd
• Depart date : yyyy-mm-dd
• Room type : NQQ, NK, SNQQ, SNQQ1, SNK, NHQQ , NHK, SNHK
• Rate code : BAR, SCPM, LPSS, GROUP~
• Track coded : LEISURE, CORPORATE, Walk-In, Meeting Room

'''
User inputs dated on 3rd May 2022

'''

User input accepted [Arrival date]
User input accepted [depart date]
User input accepted [Room type]
User input accepted [Rate code]
User input accepted [Track Code]
------------------------------------------------------------
Current date (today)          : 2022-05-03 00:00:00
Check-in date                 : 2022-05-06 00:00:00
Check-out date                : 2022-05-07 00:00:00
------------------------------------------------------------
Expected check-in days        : 3
Number of nights (stay)       : 1
Occupancy rate (today)        : 32
Occupancy rate (arrival)      : 75
ADR rate (today)              : 74.72
ADR rate (arrival)            : 138.11
------------------------------------------------------------
Room type   : NQQ 
Rate code   : BAR 
Track code  : LEISURE

i) Current date param

'''
Data of current date where user is booking the room (current day data)

'''

current year           : 2022 
current month          : 5 
current day            : 3 
current day week       : 1 
current day name       : Tuesday 
current month name     : May 
current weekend status : 0 
current holiday status : 0 
current holiday name   : NA 
current event status   : 0 
current event name     : NA

I3 - Predictions for next days...

'''
Below is the predictions for next days

'''

	Date	Month	Day	Nights	RoomType	RateCode	TrackCode	Holiday	Event	OccupancyLevel	PredictedPrice	YieldPrice	ADR	YieldRatio
0	2022-05-03	5	Tuesday	1	NQQ	BAR	LEISURE	0	0	32	94.69	83.33	74.72	-0.12
1	2022-05-04	5	Wednesday	1	NQQ	BAR	LEISURE	0	0	34	94.81	83.44	80.08	-0.12
2	2022-05-05	5	Thursday	1	NQQ	BAR	LEISURE	0	0	48	95.77	86.19	87.62	-0.10
3	2022-05-06	5	Friday	1	NQQ	BAR	LEISURE	0	1	75	95.26	101.93	138.11	0.07
4	2022-05-07	5	Saturday	1	NQQ	BAR	LEISURE	0	1	72	95.32	102.00	133.76	0.07
5	2022-05-08	5	Sunday	1	NQQ	BAR	LEISURE	0	0	11	95.04	85.53	74.62	-0.10
6	2022-05-09	5	Monday	1	NQQ	BAR	LEISURE	0	0	9	95.20	83.78	74.92	-0.12
7	2022-05-10	5	Tuesday	1	NQQ	BAR	LEISURE	0	0	3	98.86	87.00	74.53	-0.12
8	2022-05-11	5	Wednesday	1	NQQ	BAR	LEISURE	0	0	2	98.99	87.11	112.88	-0.12
9	2022-05-12	5	Thursday	1	NQQ	BAR	LEISURE	0	0	5	98.71	86.87	107.18	-0.12
10	2022-05-13	5	Friday	1	NQQ	BAR	LEISURE	0	0	11	94.85	85.36	160.30	-0.10
11	2022-05-14	5	Saturday	1	NQQ	BAR	LEISURE	0	0	11	94.87	85.39	159.11	-0.10
12	2022-05-15	5	Sunday	1	NQQ	BAR	LEISURE	0	0	5	99.99	87.99	105.71	-0.12
13	2022-05-16	5	Monday	1	NQQ	BAR	LEISURE	0	0	6	100.86	88.75	102.51	-0.12
14	2022-05-17	5	Tuesday	1	NQQ	BAR	LEISURE	0	0	7	101.17	89.03	104.74	-0.12
15	2022-05-18	5	Wednesday	1	NQQ	BAR	LEISURE	0	0	7	101.69	89.49	114.15	-0.12
16	2022-05-19	5	Thursday	1	NQQ	BAR	LEISURE	0	0	13	96.91	87.22	102.97	-0.10
17	2022-05-20	5	Friday	1	NQQ	BAR	LEISURE	0	0	9	96.80	85.18	131.24	-0.12
18	2022-05-21	5	Saturday	1	NQQ	BAR	LEISURE	0	1	6	101.72	89.51	150.29	-0.12
19	2022-05-22	5	Sunday	1	NQQ	BAR	LEISURE	0	0	5	102.71	90.39	96.99	-0.12
20	2022-05-23	5	Monday	1	NQQ	BAR	LEISURE	0	0	5	103.01	90.65	74.53	-0.12
21	2022-05-24	5	Tuesday	1	NQQ	BAR	LEISURE	0	0	5	103.17	90.79	78.99	-0.12
22	2022-05-25	5	Wednesday	1	NQQ	BAR	LEISURE	0	0	4	103.22	90.84	85.28	-0.12
23	2022-05-26	5	Thursday	1	NQQ	BAR	LEISURE	0	0	2	102.95	90.60	64.90	-0.12
24	2022-05-27	5	Friday	1	NQQ	BAR	LEISURE	0	0	4	102.95	90.60	116.73	-0.12
25	2022-05-28	5	Saturday	1	NQQ	BAR	LEISURE	0	0	6	102.82	90.48	135.59	-0.12
26	2022-05-29	5	Sunday	1	NQQ	BAR	LEISURE	0	0	5	102.82	90.48	104.31	-0.12
27	2022-05-30	5	Monday	1	NQQ	BAR	LEISURE	1	0	3	103.10	90.72	79.98	-0.12
28	2022-05-31	5	Tuesday	1	NQQ	BAR	LEISURE	0	0	2	103.10	90.72	103.33	-0.12
29	2022-06-01	6	Wednesday	1	NQQ	BAR	LEISURE	0	0	2	99.00	87.12	82.28	-0.12
30	2022-06-02	6	Thursday	1	NQQ	BAR	LEISURE	0	0	30	94.54	87.92	112.19	-0.07
31	2022-06-03	6	Friday	1	NQQ	BAR	LEISURE	0	0	39	94.31	89.59	150.65	-0.05
32	2022-06-04	6	Saturday	1	NQQ	BAR	LEISURE	0	0	54	95.57	97.48	153.80	0.02
33	2022-06-05	6	Sunday	1	NQQ	BAR	LEISURE	0	0	10	95.08	83.67	108.64	-0.12
34	2022-06-06	6	Monday	1	NQQ	BAR	LEISURE	0	0	4	98.84	86.98	112.46	-0.12
35	2022-06-07	6	Tuesday	1	NQQ	BAR	LEISURE	0	0	5	99.04	87.15	115.44	-0.12
36	2022-06-08	6	Wednesday	1	NQQ	BAR	LEISURE	0	0	1	99.15	87.25	88.70	-0.12
37	2022-06-09	6	Thursday	1	NQQ	BAR	LEISURE	0	0	4	98.70	86.85	119.43	-0.12
38	2022-06-10	6	Friday	1	NQQ	BAR	LEISURE	0	0	4	98.71	86.87	112.78	-0.12
39	2022-06-11	6	Saturday	1	NQQ	BAR	LEISURE	0	1	2	98.69	86.84	147.88	-0.12

J) Representations

In this section, predictions visualization over date factor we can analyze.

i) Main chart - Prediction + Yield + Occupancy + RateCode + Day

'''
Line chart for next days prediction with detail information

'''

Observation:

• From today to next 90 days (with time period between today to arrival/ expected check-in date) line chart is shown as above.
• It provides predicted room price, yield price, occupancy level, user input data (room type), holiday, event over future dates (next 90 days slot).

'''
Line chart for occupancy level with next days predictions

'''

Observation:

• Above is the occupancy level graph from today to next 90 days (with time period between today to arrival/ expected check-in date) line chart.
• It provides predicted room price, yield price, occupancy level, user input data (room type), holiday, event over future dates.

ii) Monthly average prices - ML Predicted price + Yield based price

'''
Bar chart for monthly averaged prediction price and yield price

'''

	PredictedPrice	YieldPrice
Months
May	99.357241	89.012759
Jun	98.413667	87.976333
Jul	99.849677	88.296774
Aug	97.320000	86.276667

Observation:

• From today to next 90 days, above horizontal bar chart shows month-wise averaged predicted room rate and month-wise averaged yield rates.
• Blue represents predictions of rates and Red represents predictions with yield applied.

iii) Day-wise average prices - ML Predicted price + Yield based price

'''
Bar chart for daily averaged prediction price and yield price

'''

	PredictedPrice	YieldPrice
Day
Monday	99.406923	87.772308
Tuesday	99.442857	87.784286
Wednesday	98.889286	87.572857
Thursday	98.720000	87.831538
Friday	98.563846	89.075385
Saturday	99.098462	90.292308
Sunday	99.935385	88.235385

Observation:

• From today to next 90 days, above horizontal bar chart shows day-wise averaged predicted room rates and day-wise averaged yield rates respectively.

iii) Event and holiday stats wise avg - ML Predicted price + Yield based price

'''
Bar chart for Event averaged prediction price and yield price

'''

	PredictedPrice	YieldPrice
Event
0	99.214382	88.049551
1	97.747500	95.070000

Observation:

• Above bar chart shows event status wise averaged predicted room rates.

'''
Bar chart for holiday averaged prediction price and yield price

'''

	PredictedPrice	YieldPrice
Holiday
0	99.106404	88.339663
1	100.150000	88.615000

Observation:

• Above bar chart shows holiday status wise averaged predicted room rates.